标题： 基金模型中的增长估计 显示英文标题

标题： Estimation of growth in fund models

摘要： 基金模型是对市场的一种统计描述，在这种模型中，所有资产的回报都可以由较低维度的一组基金的回报来表示，正交噪声除外。 或者，它们可以被描述为这样一种模型：全球增长最优投资组合仅涉及对上述基金的投资。 在局部频率估计下的基金模型由于估计误差导致的增长损失完全取决于基金的数量。 此外，在贝叶斯估计的一般滤波框架下，随着投资范围的扩大，增长损失也会增加。 提出了一种旨在以最少的偏差实现最大增长的收缩方法。 实证证据表明，与无约束的贝叶斯估计相比，收缩方法提供了一个更稳定且更接近增长潜力的估计值。 显示英文摘要

摘要： Fund models are statistical descriptions of markets where all asset returns are spanned by the returns of a lower-dimensional collection of funds, modulo orthogonal noise. Equivalently, they may be characterised as models where the global growth-optimal portfolio only involves investment in the aforementioned funds. The loss of growth due to estimation error in fund models under local frequentist estimation is determined entirely by the number of funds. Furthermore, under a general filtering framework for Bayesian estimation, the loss of growth increases as the investment universe does. A shrinkage method that targets maximal growth with the least amount of deviation is proposed. Empirical evidence suggests that shrinkage gives a stable estimate that more closely follows growth potential than an unrestricted Bayesian estimate.